Shaping i/o traffic by managing queue depth in fractional increments

ABSTRACT

A method for managing input/output (I/O) traffic in an information handling system. The method may include receiving electronic I/O requests from a network-attached server, determining a queue depth limit, monitoring latency of processed electronic I/O requests, and processing received electronic I/O requests. The number of electronic I/O requests permitted to be processed over a period of time may be based on a mathematical combination of the queue depth limit and a latency of processed electronic I/O requests. The determined queue depth limit may be a fractional value.

FIELD OF THE INVENTION

The present disclosure relates to managing input/output (I/O) requestvolume in information handling systems. Particularly, the presentdisclosure relates to increasing the efficiency of processing I/Orequests through the use of a fractionalized queue depth.

BACKGROUND OF THE INVENTION

The background description provided herein is for the purpose ofgenerally presenting the context of the disclosure. Work of thepresently named inventors, to the extent it is described in thisbackground section, as well as aspects of the description that may nototherwise qualify as prior art at the time of filing, are neitherexpressly nor impliedly admitted as prior art against the presentdisclosure.

As the value and use of information continues to increase, individualsand businesses seek additional ways to process and store information.One option available to users is information handling systems. Aninformation handling system generally processes, compiles, stores,and/or communicates information or data for business, personal, or otherpurposes thereby allowing users to take advantage of the value of theinformation. Because technology and information handling needs andrequirements vary between different users or applications, informationhandling systems may also vary regarding what information is handled,how the information is handled, how much information is processed,stored, or communicated, and how quickly and efficiently the informationmay be processed, stored, or communicated. The variations in informationhandling systems allow for information handling systems to be general orconfigured for a specific user or specific use such as financialtransaction processing, airline reservations, enterprise data storage,or global communications. In addition, information handling systems mayinclude a variety of hardware and software components that may beconfigured to process, store, and communicate information and mayinclude one or more computer systems, data storage systems, andnetworking systems.

An information handling system may process I/O read and/or writerequests sent from one or more servers to store, write, or access data.Where multiple I/O requests are sent to the system, various methods maybe used to manage the I/O requests. Some methods include usinginput/output operations per second (IOPS) or bandwidth limits. Anothermethod looks to the number of I/O requests that have been sent to thesystem but not yet completed by the system—the queue depth—to manage I/Orequest processing. Traditional queue depth management schemes, however,are often based on integer increments. Specifically, a queue depththreshold may not increase or decrease by fractional amounts undertraditional queue depth management methods, thus in some cases leadingto an inability to alter the permitted queue depth in small percentageincrements. For example, a queue depth threshold of 3 may only beincreased or decreased by a minimum of 33%, or one integer, according totraditional queue depth management methods.

Thus, there is a need in the art for improved systems and methods formanaging queue depth in fractionalized increments. Particularly, thereis a need in the art for systems and methods for using fractionalizedqueue depth to increase efficiency among servers or applications, eachwith a large volume of I/O requests.

BRIEF SUMMARY OF THE INVENTION

The following presents a simplified summary of one or more embodimentsof the present disclosure in order to provide a basic understanding ofsuch embodiments. This summary is not an extensive overview of allcontemplated embodiments, and is intended to neither identify key orcritical elements of all embodiments, nor delineate the scope of any orall embodiments.

The present disclosure, in one embodiment, relates to a method formanaging input/output (I/O) traffic in an information handling system.The method may include receiving electronic 110 requests from anetwork-attached server, determining a queue depth limit, monitoringlatency of processed electronic I/O requests, and processing receivedelectronic I/O requests. The number of electronic I/O requests permittedto be processed over a period of time may be based on a mathematicalcombination of the queue depth limit and a latency of processedelectronic I/O requests. In further embodiments, processing received I/Orequests may include managing a virtual bucket comprising a number ofvirtual tokens, each token representative of authorization for an I/Orequest to be processed, permitting a received I/O request to beprocessed if the virtual bucket comprises a number of virtual tokensgreater than zero, and decrementing the number of virtual tokens by atleast one. In particular embodiments, virtual tokens may be added to thevirtual bucket at any given time t_(x) according to the equation:Tokens(t_(x))=QueueDepthLimit*TimeElapsed(t_(x)−t_(x-1))/Latency,wherein Tokens(t_(x)) gives a value corresponding to a number of virtualtokens to be added to the virtual bucket at time t_(x); QueueDepthLimitis the determined queue depth limit; TimeElapsed(t_(x)−t_(x-1)) is anamount of time elapsed between time t_(x) and a time t_(x-1), wheret_(x-1) is a point in time prior to time t_(x); and Latency is a latencyof processed electronic I/O requests that have completed during the timeelapsed between time t_(x) and a time t_(x-1). Tokens(t_(x)) may becalculated at periodic intervals, random intervals, and/or when an I/Orequest is received. In some embodiments, the QueueDepthLimit may be afractional value.

The present disclosure, in another embodiment, also relates to a methodfor managing input/output (I/O) traffic in an information handlingsystem. The method may include receiving electronic I/O requests from aplurality of network-attached servers, determining a queue depth limitfor each of the plurality of network-attached servers, monitoringlatency of processed electronic I/O requests, and processing receivedelectronic I/O requests for each of the network-attached servers. Thenumber of electronic I/O requests permitted to be processed for a givennetwork-attached server over a period of time may be based on amathematical combination of the queue depth limit determined for thatnetwork-attached server and a latency of processed electronic I/Orequests. The queue depth limit for any one of the plurality ofnetwork-attached servers could be different than at least one other ofthe plurality of network-attached servers. Processing received I/Orequests for each of the network-attached servers may include, for eachof the network-attached servers, managing a virtual bucket comprising anumber of virtual tokens, each token representative of authorization foran I/O request from at least that network-attached server to beprocessed, permitting a received I/O request from that network-attachedserver to be processed if the virtual bucket comprises a number ofvirtual tokens greater than zero, and decrementing the number of virtualtokens by at least one. In particular embodiments, virtual tokens areadded to the virtual bucket at any given time t_(x) according to theequation: Tokens(t_(x))=QueueDepthLimit*TimeElapsed(t_(x)−t_(x-1))/Latency, wherein Tokens(t_(x)) gives a valuecorresponding to a number of virtual tokens to be added to the virtualbucket at time t_(x); QueueDepthLimit is the determined queue depthlimit for that network-attached server; TimeElapsed(t_(x)−t_(x-1)) is anamount of time elapsed between time t_(x) and a time t_(x-1), wheret_(x-1) is a point in time prior to time t_(x); and Latency is a latencyof processed electronic I/O requests that have completed during the timeelapsed between time t_(x) and a time t_(x-1). Tokens(t_(x)) may becalculated for each network-attached server at periodic intervals and/orwhen an I/O request is received from that network-attached server. Insome embodiments, the QueueDepthLimit for at least one of thenetwork-attached servers may be a fractional value.

The present disclosure, in still another embodiment, relates to aninformation handling system including a data storage system receivingI/O requests from two or more network-attached servers, and a controllermanaging the data storage system. The controller may be configured tostore a queue depth limit for each of the plurality of network-attachedservers, monitor latency of processed I/O requests, and process receivedI/O requests for each of the network-attached servers, wherein thenumber of I/O requests permitted to be processed for a givennetwork-attached server over a period of time is based on a mathematicalcombination of the queue depth limit determined for thatnetwork-attached server and a latency of processed I/O requests. In someembodiments, the queue depth limit for at least one of the plurality ofnetwork-attached servers may be a fractional value.

While multiple embodiments are disclosed, still other embodiments of thepresent disclosure will become apparent to those skilled in the art fromthe following detailed description, which shows and describesillustrative embodiments of the invention. As will be realized, thevarious embodiments of the present disclosure are capable ofmodifications in various obvious aspects, all without departing from thespirit and scope of the present disclosure. Accordingly, the drawingsand detailed description are to be regarded as illustrative in natureand not restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

While the specification concludes with claims particularly pointing outand distinctly claiming the subject matter that is regarded as formingthe various embodiments of the present disclosure, it is believed thatthe invention will be better understood from the following descriptiontaken in conjunction with the accompanying Figures, in which:

FIG. 1 is a schematic diagram of a data storage system that may benefitfrom the systems and methods for shaping I/O traffic of the presentdisclosure.

DETAILED DESCRIPTION

The present disclosure relates to novel and advantageous I/O volumemanagement in information handling systems. Particularly, the presentdisclosure relates to algorithms for managing queue depth in fractionalincrements.

For purposes of this disclosure, any system or information handlingsystem described herein may include any instrumentality or aggregate ofinstrumentalities operable to compute, calculate, determine, classify,process, transmit, receive, retrieve, originate, switch, store, display,communicate, manifest, detect, record, reproduce, handle, or utilize anyform of information, intelligence, or data for business, scientific,control, or other purposes. For example, a system or any portion thereofmay be a personal computer (e.g., desktop or laptop), tablet computer,mobile device (e.g., personal digital assistant (PDA) or smart phone),server (e.g., blade server or rack server), a network storage device, orany other suitable device or combination of devices and may vary insize, shape, performance, functionality, and price. A system may includerandom access memory (RAM), one or more processing resources such as acentral processing unit (CPU) or hardware or software control logic,ROM, and/or other types of nonvolatile memory. Additional components ofa system may include one or more disk drives or one or more mass storagedevices, one or more network ports for communicating with externaldevices as well as various input and output (I/O) devices, such as akeyboard, a mouse, touchscreen and/or a video display. Mass storagedevices may include, but are not limited to, a hard disk drive, floppydisk drive, CD-ROM drive, smart drive, flash drive, or other types ofnon-volatile data storage, a plurality of storage devices, or anycombination of storage devices. A system may include what is referred toas a user interface, which may generally include a display, mouse orother cursor control device, keyboard, button, touchpad, touch screen,microphone, camera, video recorder, speaker, LED, light, joystick,switch, buzzer, bell, and/or other user input/output device forcommunicating with one or more users or for entering information intothe system. Output devices may include any type of device for presentinginformation to a user, including but not limited to, a computer monitor,flat-screen display, or other visual display, a printer, and/or speakersor any other device for providing information in audio form, such as atelephone, a plurality of output devices, or any combination of outputdevices. A system may also include one or more buses operable totransmit communications between the various hardware components.

While the various embodiments are not limited to any particular type ofinformation handling system, the systems and methods of the presentdisclosure may be particularly useful in the context of an informationalhandling system that is or comprises a storage center comprising massstorage devices, such as but not limited to disk drive and solid statedrive systems, or virtual disk drive systems, such as that described inU.S. Pat. No. 7,613,945, titled “Virtual Disk Drive System and Method,”issued Nov. 3, 2009, U.S. Pat. No. 8,468,292, titled “Solid State DriveData Storage System and Method,” issued Jun. 18, 2013, and U.S. patentapplication Ser. No. 13/429,511, titled “Single-Level Cell andMulti-Level Cell Hybrid Solid State Drive,” filed Mar. 26, 2012, each ofwhich is incorporated by reference herein in its entirety. Such datastorage systems allow the efficient storage of data by dynamicallyallocating user data across a page pool of storage, or a matrix of drivestorage blocks, and a plurality of drives based on, for example,RAID-to-disk mapping. In general, dynamic allocation presents a virtualdisk or storage device or volume to user servers. To the server, thevolume acts the same as conventional storage, such as a disk drive, yetprovides a storage abstraction of multiple storage devices, such as RAID(redundant array of independent disks) devices, to create a dynamicallysizeable storage device. Data progression may be utilized in such diskdrive systems to move data gradually to storage space of appropriateoverall cost for the data, depending on, for example but not limited to,the data type or access patterns for the data. In general, dataprogression may determine the cost of storage in the drive systemconsidering, for example, the monetary cost of the physical storagedevices, the efficiency of the physical storage devices, and/or the RAIDlevel of logical storage devices. Based on these determination, dataprogression may move data accordingly such that data is stored on themost appropriate cost storage available. In addition, such drive systemsmay protect data from, for example, system failures or virus attacks byautomatically generating and storing snapshots or point-in-time copiesof the system or matrix of drive storage blocks at, for example,predetermined time intervals, user configured dynamic time stamps, suchas, every few minutes or hours, etc., or at times directed by theserver. These time-stamped snapshots permit the recovery of data from aprevious point in time prior to the system failure, thereby restoringthe system as it existed at that time. These snapshots or point-in-timecopies may also be used by the system or system users for otherpurposes, such as but not limited to, testing, while the main storagecan remain operational. Generally, using snapshot capabilities, a usermay view the state of a storage system as it existed in a prior point intime.

FIG. 1 illustrates one embodiment of a disk drive or data storage system100 in an information handling system environment 102, such as thatdisclosed in U.S. Pat. No. 7,613,945, U.S. Pat. No. 8,468,292, and U.S.patent application Ser. No. 13/429,511, and suitable with the variousembodiments of the present disclosure. As shown in FIG. 1, the diskdrive system 100 may include a data storage subsystem 104, which mayinclude, but is not limited to, a RAID subsystem, as will be appreciatedby those skilled in the art, and a disk or drive manager 106 having atleast one disk storage system controller. The data storage subsystem 104and disk/drive manager 106 can dynamically allocate data across drivespace of a plurality of disk drives or other suitable storage devices108, such as but not limited to optical drives, solid state drives, tapedrives, etc., based on, for example, RAID-to-disk mapping or otherstorage mapping technique. The data storage subsystem 104 may includedata storage devices distributed across one or more data sites at one ormore physical locations, which may be network connected. Any of the datasites may include original and/or replicated data (e.g., data replicatedfrom any of the other data sites) and data may be exchanged between thedata sites as desired.

In the various embodiments of the present disclosure, one or moreprograms or applications, such as a web browser, and/or otherapplications may be stored in one or more of the system memories.Programs or applications may be loaded in part or in whole into a mainmemory or processor during execution by the processor. One or moreprocessors may execute applications or programs to run systems ormethods of the present disclosure, or portions thereof, stored asexecutable programs or program code in the memory, or received from theInternet or other network. Any commercial or freeware web browser orother application capable of retrieving content from a network anddisplaying pages or screens may be used. In other embodiments, acustomized application may be used to access, display, and updateinformation.

Hardware and software components of the present disclosure, as discussedherein, may be integral portions of a single computer or server or maybe connected parts of a computer network. The hardware and softwarecomponents may be located within a single location or, in otherembodiments, portions of the hardware and software components may bedivided among a plurality of locations and connected directly or througha global computer information network, such as the Internet.

As will be appreciated by one of skill in the art, the variousembodiments of the present disclosure may be embodied as a method(including, for example, a computer-implemented process, a businessprocess, and/or any other process), apparatus (including, for example, asystem, machine, device, computer program product, and/or the like), ora combination of the foregoing. Accordingly, embodiments of the presentdisclosure may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, middleware, microcode,hardware description languages, etc.), or an embodiment combiningsoftware and hardware aspects. Furthermore, embodiments of the presentdisclosure may take the form of a computer program product on acomputer-readable medium or computer-readable storage medium, havingcomputer-executable program code embodied in the medium, that defineprocesses or methods described herein. A processor or processors mayperform the necessary tasks defined by the computer-executable programcode. Computer-executable program code for carrying out operations ofembodiments of the present disclosure may be written in an objectoriented, scripted or unscripted programming language such as Java,JavaScript, Perl, PHP, Visual Basic, Smalltalk, C++, or the like.However, the computer program code for carrying out operations ofembodiments of the present disclosure may also be written inconventional procedural programming languages, such as the C programminglanguage or similar programming languages. A code segment may representa procedure, a function, a subprogram, a program, a routine, asubroutine, a module, an object, a software package, a class, or anycombination of instructions, data structures, or program statements. Acode segment may be coupled to another code segment or a hardwarecircuit by passing and/or receiving information, data, arguments,parameters, or memory contents. Information, arguments, parameters,data, etc. may be passed, forwarded, or transmitted via any suitablemeans including memory sharing, message passing, token passing, networktransmission, etc.

In the context of this document, a computer readable medium may be anymedium that can contain, store, communicate, or transport the programfor use by or in connection with the systems disclosed herein. Thecomputer-executable program code may be transmitted using anyappropriate medium, including but not limited to the Internet, opticalfiber cable, radio frequency (RF) signals or other wireless signals, orother mediums. The computer readable medium may be, for example but isnot limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, or device. More specificexamples of suitable computer readable medium include, but are notlimited to, an electrical connection having one or more wires or atangible storage medium such as a portable computer diskette, a harddisk, a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or Flash memory), acompact disc read-only memory (CD-ROM), or other optical or magneticstorage device. Computer-readable media includes, but is not to beconfused with, computer-readable storage medium, which is intended tocover all physical, non-transitory, or similar embodiments ofcomputer-readable media.

Various embodiments of the present disclosure may be described hereinwith reference to flowchart illustrations and/or block diagrams ofmethods, apparatus (systems), and computer program products. It isunderstood that each block of the flowchart illustrations and/or blockdiagrams, and/or combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by hardware components,computer-executable program code portions, or combinations thereof.These computer-executable program code portions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a particularmachine, such that the code portions, which execute via the processor ofthe computer or other programmable data processing apparatus, createmechanisms for implementing the functions/acts specified in theflowchart and/or block diagram block or blocks. Alternatively, computerprogram implemented steps or acts may be combined with operator or humanimplemented steps or acts in order to carry out an embodiment of theinvention.

As used herein, the terms “substantially” or “generally” refer to thecomplete or nearly complete extent or degree of an action,characteristic, property, state, structure, item, or result. Forexample, an object that is “substantially” or “generally” enclosed wouldmean that the object is either completely enclosed or nearly completelyenclosed. The exact allowable degree of deviation from absolutecompleteness may in some cases depend on the specific context. However,generally speaking, the nearness of completion will be so as to havegenerally the same overall result as if absolute and total completionwere obtained. The use of “substantially” or “generally” is equallyapplicable when used in a negative connotation to refer to the completeor near complete lack of an action, characteristic, property, state,structure, item, or result. For example, an element, combination,embodiment, or composition that is “substantially free of” or “generallyfree of” an ingredient or element may still actually contain such itemas long as there is generally no measurable effect thereof.

As indicated above, in some embodiments, an information handling systemof the present disclosure may receive I/O requests from one or more, andmore typically two or more, servers, applications, or data volumes ormay include such servers, applications, or data volumes within thesystem. These I/O requests may complete in the order in which they arereceived or based on any other factor(s). However, each server,application, or volume may send I/O requests faster or more frequentlythan can be processed. As a result, I/O requests are often queued untilthey can be processed. As indicated above, such I/O requests may bemanaged by a number of methods, including for example, applying a queuedepth threshold based on, for example, a desired number of (IOPS). Morespecifically, queue depth monitoring may include tracking the number ofI/O requests that have been sent from one or more servers, applications,or volumes, but which have not yet completed by the information handlingsystem. A queue depth limit, based for example on a desired IOPS, may beestablished for the overall system and/or for each server, application,or volume to designate a maximum number of I/O requests that may bequeued at any given point in time for the system and/or for each server,application, or volume. Conventionally, a queue depth limit was aninteger value, and thus queue depth management conventionally reliedsolely on an integer-based counting system. More specifically, forexample, when an I/O request was received, the queue depth wasincremented by an integer value of one. When an I/O request completed,the queue depth was decremented by an integer value of one. If an I/Orequest was received and incrementing the queue depth caused the valueof the queue depth to exceed the established queue depth limit, that I/Owas delayed until a previously queued I/O completed. This ensured thatno more I/O requests were active than the limit imposed at any point intime. Such an integer-based system does not, however, allow for queuedepth management using fractional queue depth limits.

Accordingly, in some embodiments of the present disclosure, a tokenbucket scheme/algorithm may be used to effectively manage I/O requests,and in some embodiments, may particularly permit managing I/O requestswith a more accurate and adjustable fractional queue depth limit. Atoken bucket scheme may generally be thought of as an authorizationsystem having a virtual bucket into which virtual tokens, each tokenrepresenting an available authorization for whatever individualactions/tasks the scheme is being used to manage, may be added accordingto a determined method or at a determined rate. According to oneembodiment of the present disclosure, when an I/O request is received bythe information handling system, for example from a server, application,or volume, an available token may be removed from the bucket. When theinformation handling system completes an I/O request, a token may bereturned to the bucket. When the bucket is empty of tokens, incoming I/Orequests may be held or delayed until a token becomes available, such aswhen a prior I/O request has completed and its token is returned to thebucket. As may be appreciated, the maximum number of tokens for thebucket, available when no I/O requests are processing, represents aqueue depth limit. In such an embodiment, however, the queue depth limitmay only be managed to integer values.

Accordingly, in other embodiments of the present disclosure, virtualtokens may be added to the virtual bucket at a predetermined or othercalculated or determined rate. As with the embodiments described above,when an I/O request is received by the information handling system, anavailable token may be removed from the bucket. When there are no moretokens in the bucket, however, incoming I/O requests may be held untilmore tokens are added to the bucket. According to some embodiments ofthe present disclosure, tokens may be added pursuant to a predeterminedor calculated rate. The rate at which tokens are added to the bucket maybe based on any number of various parameters or events, and may be addedaccording to any regular, irregular, or random schedule or intervals.

In one example embodiment, the number of tokens that are added to thebucket at any given point in time, t_(x), may be based on the followingequation:

Tokens(t _(x))=QueueDepthLimit*TimeElapsed(t _(x) −t _(x-1))/Latency

where Tokens(t_(x)) (also referred to herein simply as Tokens) is thenumber of virtual tokens to be added to the virtual bucket at timet_(x); QueueDepthLimit is a determined, and in some cases predetermined,queue depth threshold or limit; TimeElapsed(t_(x)−t_(x-1)) is the amountof time elapsed between time t_(x) and time t_(x-1), where t_(x-1) is apoint in time prior to time t_(x), and in some cases is the time atwhich the most recent Tokens calculation was performed; and Latency is avalue corresponding to the latency of I/O requests that have completedduring the TimeElapsed period. While described above as having theparameters QueueDepthLimit, TimeElapsed, and Latency, any additionalparameters may be included in the Tokens equation, and Tokens is notlimited to equations having solely the QueueDepthLimit, TimeElapsed, andLatency parameters. Likewise, while Tokens is indicated above as being aparticular mathematical combination of QueueDepthLimit, TimeElapsed, andLatency, other mathematical combinations of QueueDepthLimit,TimeElapsed, and Latency, alone or with other parameters, may beutilized and Tokens is not limited to only the particular mathematicalcombination indicated above. Furthermore, in some embodiments, theresult of Tokens may be rounded, such as rounded to the nearest integer,always rounded up, always rounded down, or rounded in any suitablemanner. In other embodiments, Tokens need not be rounded, and thevirtual bucket need not hold only whole (i.e., integer) tokens. Stillfurther, while described as analogous to a bucket, the virtual bucketdescribed herein may simply be represented by a variable storing anumerical value corresponding to the number of virtual tokens currentlyheld by the bucket, or by any other suitable data representation.

The QueueDepthLimit may be any suitable numerical value, including anyfractional value. The QueueDepthLimit may be a predetermined orpreselected value or may be based on one or more parameters and/orrequirements relating to the information handling system and/or theservers, applications, or volumes from which I/O requests are beingreceived, and/or any other suitable factor or factors. TheQueueDepthLimit may be a static value or may change dynamically based onany of such parameters or requirements or other suitable factor orfactors.

In some embodiments, Tokens may be calculated periodically according toa predetermined schedule or period. Tokens may additionally oralternatively be calculated, for example, each time an I/O request isreceived and/or completed or may be based on the satisfaction of anyother event or parameter or combinations thereof. In additional oralternative embodiments, Tokens may be calculated at any other regular,irregular, or random intervals or times. As will be recognized, thevalue for TimeElapsed may vary for any or each calculation.

In some embodiments, Latency may be a smoothed latency value accordingto a suitable latency smoothing algorithm. An average latency value mayalternatively be used.

A scheme for managing I/O requests as described above may dynamicallymanage queue depth, increasing efficiency of the information handlingsystem. For example, in general, a low latency, and thus a low Latencyvalue for the Tokens equation, may reflect availability for a relativelylarger processing load. As such, when Tokens is calculated, more virtualtokens will be added to the bucket, thereby permitting additional I/Orequests. Conversely, in general, a high latency, and thus a highLatency value for the Tokens equation, may indicate that the system isalready under a relatively higher processing load. As such, when Tokensis calculated, fewer virtual tokens (and in some cases no tokens) willbe added to the bucket, thereby queuing more I/O requests. As a result,such a scheme allows the information handling system to dynamicallymanage I/O requests based on actual, current performance of the system.

As described above, use of a token bucket scheme as described to manageI/O requests and queue depth additionally permits management tofractional queue depth limits. As further described above, in general,the QueueDepthLimit may be a predetermined or preselected value or maybe based on one or more parameters and/or requirements relating to theinformation handling system and/or the servers, applications, or volumesfrom which I/O requests are being received, and/or any other suitablefactor or factors. In one embodiment, the QueueDepthLimit may berepresentative of, although not necessarily exactly, a queue depth ofprocessing I/Os permitted at any given time. In further embodiments, theQueueDepthLimit may be selected based on a desired average queue depthto be permitted. Permitting QueueDepthLimit to be fractional allows forincremental changes in the desired average queue depth.

As a result of the various embodiments described above, throughput andlatency of the information handling system as a whole may remaingenerally within an efficient range, despite whether the system isexperiencing a heavy or light load. Additionally, the above embodimentspermit temporary bursts of requests that would otherwise exceed aconventional static queue depth limit to be handled more efficiently.

Furthermore, managing I/O requests via queue depth according to theabove algorithm embodiments may allow more efficient balancing of I/Oacross servers, applications, or volumes of varying capabilities and/ordesignated importance. Specifically, performance between variousservers, applications, and/or volumes may be managed or balanced bymanipulating the QueueDepthLimit for each server, application, and/orvolume, or combinations thereof. In some embodiments, an overallinformation handling system queue depth may be determined based on, forexample, latency of the system or other parameter or parameters, and thesystem queue depth may be divided among various servers, applications,and/or volumes within the system. Allocation of queue depth among two ormore servers, applications, or volumes may be based on, for example,relative importance and/or latency of the individual servers,applications, or volumes, or of particular drives, disks, or otherphysical storage devices allocated thereto. However, as noted above, invarious embodiments, the latencies of the physical storage devices canaffect the calculation of Tokens and thus may naturally distributeresources relatively fairly based on capability and demand.

The above described embodiments for shaping I/O traffic are advantageousover conventional methods such as managing I/O based on the system IOPSor bandwidth limits. For example, according to the various embodimentsdescribed herein, I/O requests may be balanced according to a more fairweighting among servers, applications, or volumes competing for theresources. As a specific example, under a conventional I/O managementscheme queuing I/O requests based on a threshold IOPS, a first serversending several write requests (typically consuming relatively moreprocessing time) and a second server sending small sequential readrequests (typically consuming relatively less processing time) will bothbe limited by the long latency of the resource intensive operations ofthe first server. Conversely, managing I/O traffic according to variousembodiments disclosed herein can distinguish between relatively more“expensive” I/O requests (in the terms of processing time per I/Orequest) and relatively less “expensive” I/O requests via the Latencyparameter in the Tokens equation, where a low latency would generallytranslate to availability to take on additional processing load, withoutregard to the actual number of I/O requests being processed.

In the foregoing description various embodiments of the presentdisclosure have been presented for the purpose of illustration anddescription. They are not intended to be exhaustive or to limit theinvention to the precise form disclosed. Obvious modifications orvariations are possible in light of the above teachings. The variousembodiments were chosen and described to provide the best illustrationof the principals of the disclosure and their practical application, andto enable one of ordinary skill in the art to utilize the variousembodiments with various modifications as are suited to the particularuse contemplated. All such modifications and variations are within thescope of the present disclosure as determined by the appended claimswhen interpreted in accordance with the breadth they are fairly,legally, and equitably entitled.

1-20. (canceled)
 21. A method for managing input/output (I/O) traffic inan information handling system, the method comprising: monitoringlatency of previously processed electronic I/O requests; determining aqueue depth limit; receiving additional electronic I/O requests from anetwork-attached server; and processing the additional electronic I/Orequests, wherein the number of additional electronic I/O requestspermitted to be processed over a period of time is based on amathematical combination of the queue depth limit and the latency ofpreviously processed electronic I/O requests.
 22. The method of claim21, wherein processing the additional electronic I/O requests comprises:managing a virtual bucket comprising a number of virtual tokens, eachtoken representative of authorization for an I/O request to beprocessed; and permitting one of the additional electronic I/O requeststo be processed if the virtual bucket comprises a number of virtualtokens greater than zero, and decrementing the number of virtual tokensby at least one.
 23. The method of claim 22, wherein calculating anumber of virtual tokens to add to the virtual bucket at any given timet_(x) is performed according to the equation:Tokens(t _(x))=QueueDepthLimit*TimeElapsed(t _(x) −t _(1-x))/Latencywherein Tokens(t_(x)) gives a value corresponding to a number of virtualtokens to be added to the virtual bucket at time t_(x); QueueDepthLimitis the determined queue depth limit; TimeElapsed(t_(x)−t_(x-1)) is anamount of time elapsed between time t_(x) and a time t_(x-1), wheret_(x-1) is a point in time prior to time t_(x); and Latency is a latencyof previously processed electronic I/O requests that have completedduring the time elapsed between time t_(x) and a time t_(x-1).
 24. Themethod of claim 21, wherein the mathematical combination of the queuedepth limit and the latency of previously processed electronic I/Orequests is calculated at periodic intervals.
 25. The method of claim21, wherein the mathematical combination of the queue depth limit andthe latency of previously processed electronic I/O requests iscalculated at random intervals.
 26. The method of claim 21, wherein themathematical combination of the queue depth limit and the latency ofpreviously processed electronic I/O requests is calculated when anelectronic I/O request is received.
 27. The method of claim 24, whereinthe mathematical combination of the queue depth limit and the latency ofpreviously processed electronic I/O requests is additionally calculatedwhen an electronic I/O request is received.
 28. The method of claim 23,wherein the QueueDepthLimit is a fractional value.
 29. The method ofclaim 21, wherein the queue depth limit is a fractional value.
 30. Themethod of claim 22, wherein processing the additional electronic I/Orequests further comprising waiting to process a received I/O request ifthe virtual bucket comprises a number of virtual tokens no more thanzero until the virtual bucket receives additional virtual tokens.
 31. Amethod for managing input/output (I/O) traffic in an informationhandling system, the method comprising: monitoring latency of previouslyprocessed electronic I/O requests, the electronic I/O requests havingbeen received from a plurality of network-attached servers; determininga queue depth limit for each of the plurality of network-attachedservers; receiving additional electronic I/O requests from the pluralityof network-attached servers; and processing the additional electronicI/O requests for each of the network-attached servers, wherein thenumber of additional electronic I/O requests permitted to be processedfor a given network-attached server over a period of time is based on amathematical combination of the queue depth limit determined for thatnetwork-attached server and the latency of previously processedelectronic I/O requests.
 32. The method of claim 31, wherein the queuedepth limit for one of the plurality of network-attached servers isdifferent than at least one other of the plurality of network-attachedservers.
 33. The method of claim 31, wherein processing received I/Orequests for each of the network-attached servers comprises, for each ofthe network-attached servers: managing a virtual bucket comprising anumber of virtual tokens, each token representative of authorization foran I/O request from at least that network-attached server to beprocessed; and permitting one of the additional electronic I/O requestfrom that network-attached server to be processed if the virtual bucketcomprises a number of virtual tokens greater than zero, and decrementingthe number of virtual tokens by at least one.
 34. The method of claim36, wherein calculating a number of virtual tokens to add to the virtualbucket at any given time t_(x) is performed according to the equation:Tokens(t _(x))=QueueDepthLimit*TimeElapsed(t _(x) −t _(x-1))/Latencywherein Tokens(t_(x)) gives a value corresponding to a number of virtualtokens to be added to the virtual bucket at time t_(x); QueueDepthLimitis the determined queue depth limit for that network-attached server;TimeElapsed(t_(x)−t_(x-1)) is an amount of time elapsed between timet_(x) and a time t_(x-1), where t_(x-1) is a point in time prior to timet_(x); and Latency is a latency of previously processed electronic I/Orequests that have completed during the time elapsed between time t_(x)and a time t_(x-1).
 35. The method of claim 31, wherein the mathematicalcombination is calculated for a given network-attached server when anI/O request is received from that network-attached server.
 36. Themethod of claim 31, wherein the mathematical combination is calculatedfor each network-attached server at periodic intervals.
 37. The methodof claim 34, wherein the QueueDepthLimit for at least one of thenetwork-attached servers is a fractional value.
 38. The method of claim31, wherein the queue depth limit for at least one of thenetwork-attached servers is a fractional value.
 39. An informationhandling system comprising: a data storage system receiving I/O requestsfrom two or more network-attached servers; and a controller managing thedata storage system, including: monitoring latency of previouslyprocessed electronic I/O requests; storing a queue depth limit for eachof the two or more network-attached servers; receiving additionalelectronic I/O requests from the two or more network-attached servers;and processing the additional electronic I/O requests for each of thenetwork-attached servers, wherein the number of additional electronicI/O requests permitted to be processed for a given network-attachedserver over a period of time is based on a mathematical combination ofthe queue depth limit determined for that network-attached server andthe latency of previously processed electronic I/O requests.
 40. Theinformation handling system of claim 39, wherein the queue depth limitfor at least one of the two or more network-attached servers is afractional value.